Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory607.5 KiB
Average record size in memory311.0 B

Variable types

Numeric19
Categorical13
Boolean1

Alerts

01.SES is highly overall correlated with 02.SESHigh correlation
02.SES is highly overall correlated with 01.SESHigh correlation
Counting-02 is highly overall correlated with Place Value-02High correlation
Disability_Cognitive is highly overall correlated with Disability_Non-disableHigh correlation
Disability_Non-disable is highly overall correlated with Disability_CognitiveHigh correlation
HRSIW-01-SOY is highly overall correlated with TextLevel-01-EOY and 5 other fieldsHigh correlation
NumSibling is highly overall correlated with SiblingOrderHigh correlation
Place Value-02 is highly overall correlated with Counting-02High correlation
SiblingOrder is highly overall correlated with NumSiblingHigh correlation
TextLevel-01-EOY is highly overall correlated with HRSIW-01-SOY and 6 other fieldsHigh correlation
TextLevel-01-MOY is highly overall correlated with HRSIW-01-SOY and 5 other fieldsHigh correlation
TextLevel-01-SOY is highly overall correlated with HRSIW-01-SOY and 6 other fieldsHigh correlation
TextLevel-02-EOY is highly overall correlated with TextLevel-01-EOY and 4 other fieldsHigh correlation
TextLevel-02-MOY is highly overall correlated with HRSIW-01-SOY and 5 other fieldsHigh correlation
TextLevel-02-SOY is highly overall correlated with HRSIW-01-SOY and 6 other fieldsHigh correlation
WritingVocab-01-SOY is highly overall correlated with HRSIW-01-SOY and 3 other fieldsHigh correlation
Place Value-01 is highly imbalanced (51.6%) Imbalance
Disability_Physical is highly imbalanced (78.8%) Imbalance
Disability_Sensory is highly imbalanced (95.5%) Imbalance
Disability_SocialEmotional is highly imbalanced (77.4%) Imbalance
NCCD-Funded is highly imbalanced (56.7%) Imbalance
TextLevel-01-SOY has 36 (1.8%) zeros Zeros
Counting-01 has 155 (7.8%) zeros Zeros
Place Value-02 has 26 (1.3%) zeros Zeros
Addition and Subtraction-01 has 382 (19.1%) zeros Zeros
Addition and Subtraction-02 has 84 (4.2%) zeros Zeros
Multiplication and Division-02 has 161 (8.1%) zeros Zeros

Reproduction

Analysis started2025-05-27 14:34:47.088832
Analysis finished2025-05-27 14:35:50.951509
Duration1 minute and 3.86 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

TextLevel-01-SOY
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.705
Minimum0
Maximum31
Zeros36
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:51.059281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median10
Q314
95-th percentile22
Maximum31
Range31
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0697938
Coefficient of variation (CV)0.56700549
Kurtosis0.66392915
Mean10.705
Median Absolute Deviation (MAD)4
Skewness0.76171154
Sum21410
Variance36.842396
MonotonicityNot monotonic
2025-05-27T14:35:51.202516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 149
 
7.4%
10 146
 
7.3%
7 138
 
6.9%
11 137
 
6.9%
8 136
 
6.8%
12 127
 
6.3%
6 117
 
5.9%
13 111
 
5.5%
5 103
 
5.1%
15 91
 
4.5%
Other values (22) 745
37.2%
ValueCountFrequency (%)
0 36
 
1.8%
1 37
 
1.8%
2 57
 
2.9%
3 77
3.9%
4 77
3.9%
5 103
5.1%
6 117
5.9%
7 138
6.9%
8 136
6.8%
9 149
7.4%
ValueCountFrequency (%)
31 12
0.6%
30 4
 
0.2%
29 7
 
0.4%
28 8
0.4%
27 12
0.6%
26 11
0.5%
25 13
0.7%
24 17
0.9%
23 11
0.5%
22 19
0.9%

TextLevel-01-MOY
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.9945
Minimum0
Maximum31
Zeros6
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:51.342460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q111
median14
Q318
95-th percentile26
Maximum31
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.4681276
Coefficient of variation (CV)0.36467556
Kurtosis0.61744827
Mean14.9945
Median Absolute Deviation (MAD)3
Skewness0.63331078
Sum29989
Variance29.90042
MonotonicityNot monotonic
2025-05-27T14:35:51.468986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
14 179
 
8.9%
15 174
 
8.7%
13 174
 
8.7%
12 169
 
8.5%
16 137
 
6.9%
11 134
 
6.7%
17 129
 
6.5%
10 118
 
5.9%
18 113
 
5.7%
19 80
 
4.0%
Other values (22) 593
29.6%
ValueCountFrequency (%)
0 6
 
0.3%
1 1
 
0.1%
2 3
 
0.1%
3 1
 
0.1%
4 18
 
0.9%
5 20
 
1.0%
6 24
 
1.2%
7 45
2.2%
8 61
3.0%
9 75
3.8%
ValueCountFrequency (%)
31 21
1.1%
30 14
 
0.7%
29 18
0.9%
28 19
0.9%
27 22
1.1%
26 21
1.1%
25 17
0.9%
24 33
1.7%
23 35
1.8%
22 36
1.8%

TextLevel-01-EOY
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.121
Minimum4
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:51.591639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15
Q118
median21
Q324
95-th percentile29.05
Maximum31
Range27
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5574932
Coefficient of variation (CV)0.21578018
Kurtosis0.20944907
Mean21.121
Median Absolute Deviation (MAD)3
Skewness0.04210954
Sum42242
Variance20.770744
MonotonicityNot monotonic
2025-05-27T14:35:51.705383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
19 219
10.9%
20 186
 
9.3%
18 174
 
8.7%
21 169
 
8.5%
22 165
 
8.2%
17 160
 
8.0%
23 127
 
6.3%
24 115
 
5.8%
16 95
 
4.8%
25 93
 
4.7%
Other values (18) 497
24.9%
ValueCountFrequency (%)
4 2
 
0.1%
5 2
 
0.1%
6 1
 
0.1%
7 5
 
0.2%
8 4
 
0.2%
9 6
 
0.3%
10 10
0.5%
11 10
0.5%
12 14
0.7%
13 17
0.9%
ValueCountFrequency (%)
31 43
 
2.1%
30 57
 
2.9%
29 56
 
2.8%
28 63
 
3.1%
27 56
 
2.8%
26 80
4.0%
25 93
4.7%
24 115
5.8%
23 127
6.3%
22 165
8.2%

TextLevel-02-SOY
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.7585
Minimum2
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:51.826607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile13
Q118
median22
Q325
95-th percentile30
Maximum31
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1650282
Coefficient of variation (CV)0.23737979
Kurtosis-0.18786385
Mean21.7585
Median Absolute Deviation (MAD)4
Skewness-0.19487959
Sum43517
Variance26.677517
MonotonicityNot monotonic
2025-05-27T14:35:51.945214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
19 170
 
8.5%
22 150
 
7.5%
21 143
 
7.1%
20 135
 
6.8%
23 135
 
6.8%
24 126
 
6.3%
17 121
 
6.0%
25 119
 
5.9%
18 113
 
5.7%
31 94
 
4.7%
Other values (19) 694
34.7%
ValueCountFrequency (%)
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
6 5
 
0.2%
7 5
 
0.2%
8 5
 
0.2%
9 8
 
0.4%
10 10
0.5%
11 16
0.8%
12 22
1.1%
ValueCountFrequency (%)
31 94
4.7%
30 66
3.3%
29 78
3.9%
28 86
4.3%
27 77
3.9%
26 94
4.7%
25 119
5.9%
24 126
6.3%
23 135
6.8%
22 150
7.5%

TextLevel-02-MOY
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.052
Minimum5
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:52.070553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile16
Q121
median24
Q328
95-th percentile31
Maximum31
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.6571601
Coefficient of variation (CV)0.19362881
Kurtosis0.34008569
Mean24.052
Median Absolute Deviation (MAD)3
Skewness-0.64697631
Sum48104
Variance21.689141
MonotonicityNot monotonic
2025-05-27T14:35:52.205378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
23 183
 
9.2%
25 177
 
8.8%
24 161
 
8.1%
28 157
 
7.8%
26 142
 
7.1%
30 135
 
6.8%
27 127
 
6.3%
22 126
 
6.3%
31 125
 
6.2%
29 124
 
6.2%
Other values (16) 543
27.2%
ValueCountFrequency (%)
5 2
 
0.1%
7 2
 
0.1%
8 7
 
0.4%
9 1
 
0.1%
10 4
 
0.2%
11 4
 
0.2%
12 18
0.9%
13 13
0.7%
14 16
0.8%
15 19
0.9%
ValueCountFrequency (%)
31 125
6.2%
30 135
6.8%
29 124
6.2%
28 157
7.8%
27 127
6.3%
26 142
7.1%
25 177
8.8%
24 161
8.1%
23 183
9.2%
22 126
6.3%

TextLevel-02-EOY
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.955
Minimum5
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:52.317313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q125
median28
Q330
95-th percentile31
Maximum31
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.710911
Coefficient of variation (CV)0.1376706
Kurtosis3.9466516
Mean26.955
Median Absolute Deviation (MAD)2
Skewness-1.6360303
Sum53910
Variance13.77086
MonotonicityNot monotonic
2025-05-27T14:35:52.433759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
29 310
15.5%
30 309
15.4%
31 245
12.2%
28 226
11.3%
27 182
9.1%
26 163
8.2%
25 143
7.1%
24 123
 
6.2%
23 104
 
5.2%
22 52
 
2.6%
Other values (16) 143
7.1%
ValueCountFrequency (%)
5 1
 
0.1%
6 1
 
0.1%
8 1
 
0.1%
9 3
 
0.1%
10 2
 
0.1%
11 2
 
0.1%
12 4
 
0.2%
13 6
0.3%
14 7
0.4%
15 10
0.5%
ValueCountFrequency (%)
31 245
12.2%
30 309
15.4%
29 310
15.5%
28 226
11.3%
27 182
9.1%
26 163
8.2%
25 143
7.1%
24 123
 
6.2%
23 104
 
5.2%
22 52
 
2.6%

WritingVocab-01-SOY
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.0185
Minimum0
Maximum95
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:52.568784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q113
median20
Q329
95-th percentile46
Maximum95
Range95
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.679098
Coefficient of variation (CV)0.57583843
Kurtosis1.4821392
Mean22.0185
Median Absolute Deviation (MAD)8
Skewness0.98078114
Sum44037
Variance160.75954
MonotonicityNot monotonic
2025-05-27T14:35:52.715431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 80
 
4.0%
14 79
 
4.0%
17 78
 
3.9%
16 74
 
3.7%
15 68
 
3.4%
13 67
 
3.4%
11 66
 
3.3%
23 66
 
3.3%
20 65
 
3.2%
12 62
 
3.1%
Other values (63) 1295
64.8%
ValueCountFrequency (%)
0 5
 
0.2%
1 7
 
0.4%
2 13
 
0.7%
3 24
1.2%
4 30
1.5%
5 28
1.4%
6 47
2.4%
7 47
2.4%
8 49
2.5%
9 43
2.1%
ValueCountFrequency (%)
95 1
0.1%
84 1
0.1%
81 1
0.1%
80 1
0.1%
74 1
0.1%
71 1
0.1%
67 2
0.1%
66 1
0.1%
65 2
0.1%
64 1
0.1%

HRSIW-01-SOY
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.513
Minimum0
Maximum58
Zeros10
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:52.873273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q125
median32
Q337
95-th percentile46
Maximum58
Range58
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.004943
Coefficient of variation (CV)0.32789115
Kurtosis0.35425807
Mean30.513
Median Absolute Deviation (MAD)6
Skewness-0.53538087
Sum61026
Variance100.09888
MonotonicityNot monotonic
2025-05-27T14:35:53.018618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 113
 
5.7%
31 99
 
5.0%
32 96
 
4.8%
34 94
 
4.7%
37 92
 
4.6%
30 88
 
4.4%
35 87
 
4.3%
27 83
 
4.2%
36 81
 
4.0%
29 79
 
4.0%
Other values (47) 1088
54.4%
ValueCountFrequency (%)
0 10
0.5%
1 3
 
0.1%
2 5
 
0.2%
3 6
0.3%
4 8
0.4%
5 5
 
0.2%
6 4
 
0.2%
7 14
0.7%
8 6
0.3%
9 10
0.5%
ValueCountFrequency (%)
58 1
 
0.1%
55 1
 
0.1%
54 3
 
0.1%
53 4
 
0.2%
52 4
 
0.2%
51 11
0.5%
50 7
 
0.4%
49 8
 
0.4%
48 23
1.1%
47 20
1.0%

Counting-01
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.758
Minimum0
Maximum5
Zeros155
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:53.135588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93588137
Coefficient of variation (CV)0.53235573
Kurtosis0.37686179
Mean1.758
Median Absolute Deviation (MAD)1
Skewness0.33322424
Sum3516
Variance0.87587394
MonotonicityNot monotonic
2025-05-27T14:35:53.237705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 891
44.5%
1 610
30.5%
3 261
 
13.1%
0 155
 
7.8%
4 74
 
3.7%
5 9
 
0.4%
ValueCountFrequency (%)
0 155
 
7.8%
1 610
30.5%
2 891
44.5%
3 261
 
13.1%
4 74
 
3.7%
5 9
 
0.4%
ValueCountFrequency (%)
5 9
 
0.4%
4 74
 
3.7%
3 261
 
13.1%
2 891
44.5%
1 610
30.5%
0 155
 
7.8%

Counting-02
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8505
Minimum0
Maximum6
Zeros15
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:53.319765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0727186
Coefficient of variation (CV)0.37632646
Kurtosis-0.45527574
Mean2.8505
Median Absolute Deviation (MAD)1
Skewness0.14659288
Sum5701
Variance1.1507251
MonotonicityNot monotonic
2025-05-27T14:35:53.411229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 678
33.9%
3 594
29.7%
4 445
22.2%
1 140
 
7.0%
5 125
 
6.2%
0 15
 
0.8%
6 3
 
0.1%
ValueCountFrequency (%)
0 15
 
0.8%
1 140
 
7.0%
2 678
33.9%
3 594
29.7%
4 445
22.2%
5 125
 
6.2%
6 3
 
0.1%
ValueCountFrequency (%)
6 3
 
0.1%
5 125
 
6.2%
4 445
22.2%
3 594
29.7%
2 678
33.9%
1 140
 
7.0%
0 15
 
0.8%

Place Value-01
Categorical

Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
1
1481 
2
268 
0
238 
3
 
12
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 1481
74.1%
2 268
 
13.4%
0 238
 
11.9%
3 12
 
0.6%
4 1
 
0.1%

Length

2025-05-27T14:35:53.521457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:53.602358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1481
74.1%
2 268
 
13.4%
0 238
 
11.9%
3 12
 
0.6%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 1481
74.1%
2 268
 
13.4%
0 238
 
11.9%
3 12
 
0.6%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1481
74.1%
2 268
 
13.4%
0 238
 
11.9%
3 12
 
0.6%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1481
74.1%
2 268
 
13.4%
0 238
 
11.9%
3 12
 
0.6%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1481
74.1%
2 268
 
13.4%
0 238
 
11.9%
3 12
 
0.6%
4 1
 
< 0.1%

Place Value-02
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6815
Minimum0
Maximum5
Zeros26
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:53.687265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.63973618
Coefficient of variation (CV)0.38045565
Kurtosis1.4172891
Mean1.6815
Median Absolute Deviation (MAD)0
Skewness0.43478249
Sum3363
Variance0.40926238
MonotonicityNot monotonic
2025-05-27T14:35:53.787316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1140
57.0%
1 723
36.1%
3 85
 
4.2%
0 26
 
1.3%
4 25
 
1.2%
5 1
 
0.1%
ValueCountFrequency (%)
0 26
 
1.3%
1 723
36.1%
2 1140
57.0%
3 85
 
4.2%
4 25
 
1.2%
5 1
 
0.1%
ValueCountFrequency (%)
5 1
 
0.1%
4 25
 
1.2%
3 85
 
4.2%
2 1140
57.0%
1 723
36.1%
0 26
 
1.3%

Addition and Subtraction-01
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2905
Minimum0
Maximum5
Zeros382
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:53.873312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93517219
Coefficient of variation (CV)0.72465881
Kurtosis1.0307923
Mean1.2905
Median Absolute Deviation (MAD)1
Skewness0.74109595
Sum2581
Variance0.87454702
MonotonicityNot monotonic
2025-05-27T14:35:53.961853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 868
43.4%
2 602
30.1%
0 382
19.1%
3 91
 
4.5%
4 49
 
2.5%
5 8
 
0.4%
ValueCountFrequency (%)
0 382
19.1%
1 868
43.4%
2 602
30.1%
3 91
 
4.5%
4 49
 
2.5%
5 8
 
0.4%
ValueCountFrequency (%)
5 8
 
0.4%
4 49
 
2.5%
3 91
 
4.5%
2 602
30.1%
1 868
43.4%
0 382
19.1%

Addition and Subtraction-02
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2575
Minimum0
Maximum6
Zeros84
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:54.049725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1557948
Coefficient of variation (CV)0.51197999
Kurtosis0.0037244821
Mean2.2575
Median Absolute Deviation (MAD)1
Skewness0.52799279
Sum4515
Variance1.3358617
MonotonicityNot monotonic
2025-05-27T14:35:54.135990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 943
47.1%
1 364
 
18.2%
4 261
 
13.1%
3 260
 
13.0%
5 87
 
4.3%
0 84
 
4.2%
6 1
 
0.1%
ValueCountFrequency (%)
0 84
 
4.2%
1 364
 
18.2%
2 943
47.1%
3 260
 
13.0%
4 261
 
13.1%
5 87
 
4.3%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 87
 
4.3%
4 261
 
13.1%
3 260
 
13.0%
2 943
47.1%
1 364
 
18.2%
0 84
 
4.2%
Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
2
753 
0
621 
1
617 
3
 
8
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row0
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 753
37.6%
0 621
31.1%
1 617
30.9%
3 8
 
0.4%
4 1
 
0.1%

Length

2025-05-27T14:35:54.254481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:54.333450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 753
37.6%
0 621
31.1%
1 617
30.9%
3 8
 
0.4%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 753
37.6%
0 621
31.1%
1 617
30.9%
3 8
 
0.4%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 753
37.6%
0 621
31.1%
1 617
30.9%
3 8
 
0.4%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 753
37.6%
0 621
31.1%
1 617
30.9%
3 8
 
0.4%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 753
37.6%
0 621
31.1%
1 617
30.9%
3 8
 
0.4%
4 1
 
< 0.1%

Multiplication and Division-02
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7975
Minimum0
Maximum5
Zeros161
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:54.420618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.77187541
Coefficient of variation (CV)0.42941608
Kurtosis1.8447515
Mean1.7975
Median Absolute Deviation (MAD)0
Skewness-0.31454217
Sum3595
Variance0.59579165
MonotonicityNot monotonic
2025-05-27T14:35:54.517346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 1369
68.5%
1 302
 
15.1%
0 161
 
8.1%
3 120
 
6.0%
4 45
 
2.2%
5 3
 
0.1%
ValueCountFrequency (%)
0 161
 
8.1%
1 302
 
15.1%
2 1369
68.5%
3 120
 
6.0%
4 45
 
2.2%
5 3
 
0.1%
ValueCountFrequency (%)
5 3
 
0.1%
4 45
 
2.2%
3 120
 
6.0%
2 1369
68.5%
1 302
 
15.1%
0 161
 
8.1%

Kinder_Age
Real number (ℝ)

Distinct514
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2768055
Minimum4.5041096
Maximum6.5342466
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:54.637844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.5041096
5-th percentile4.6958904
Q15.0157534
median5.2821918
Q35.5342466
95-th percentile5.8438356
Maximum6.5342466
Range2.030137
Interquartile range (IQR)0.51849315

Descriptive statistics

Standard deviation0.34725092
Coefficient of variation (CV)0.065807035
Kurtosis-0.53109574
Mean5.2768055
Median Absolute Deviation (MAD)0.25753425
Skewness0.03306585
Sum10553.611
Variance0.1205832
MonotonicityNot monotonic
2025-05-27T14:35:54.813100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.257534247 13
 
0.7%
5.15890411 13
 
0.7%
5.328767123 13
 
0.7%
5.032876712 12
 
0.6%
5.331506849 12
 
0.6%
4.75890411 10
 
0.5%
5.347945205 10
 
0.5%
5.668493151 10
 
0.5%
5.504109589 10
 
0.5%
4.983561644 9
 
0.4%
Other values (504) 1888
94.4%
ValueCountFrequency (%)
4.504109589 2
0.1%
4.506849315 1
 
0.1%
4.512328767 1
 
0.1%
4.517808219 4
0.2%
4.520547945 1
 
0.1%
4.526027397 3
0.1%
4.539726027 4
0.2%
4.547945205 2
0.1%
4.550684932 1
 
0.1%
4.553424658 1
 
0.1%
ValueCountFrequency (%)
6.534246575 1
0.1%
6.279452055 1
0.1%
6.246575342 1
0.1%
6.2 1
0.1%
6.153424658 1
0.1%
6.150684932 1
0.1%
6.147945205 1
0.1%
6.120547945 1
0.1%
6.115068493 1
0.1%
6.109589041 1
0.1%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
Male
1018 
Female
982 

Length

Max length6
Median length4
Mean length4.982
Min length4

Characters and Unicode

Total characters9964
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 1018
50.9%
Female 982
49.1%

Length

2025-05-27T14:35:54.957172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:55.036283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 1018
50.9%
female 982
49.1%

Most occurring characters

ValueCountFrequency (%)
e 2982
29.9%
a 2000
20.1%
l 2000
20.1%
M 1018
 
10.2%
F 982
 
9.9%
m 982
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2982
29.9%
a 2000
20.1%
l 2000
20.1%
M 1018
 
10.2%
F 982
 
9.9%
m 982
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2982
29.9%
a 2000
20.1%
l 2000
20.1%
M 1018
 
10.2%
F 982
 
9.9%
m 982
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2982
29.9%
a 2000
20.1%
l 2000
20.1%
M 1018
 
10.2%
F 982
 
9.9%
m 982
 
9.9%

Disability_Non-disable
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
1
1381 
0
619 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1381
69.0%
0 619
30.9%

Length

2025-05-27T14:35:55.119030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:55.182255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1381
69.0%
0 619
30.9%

Most occurring characters

ValueCountFrequency (%)
1 1381
69.0%
0 619
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1381
69.0%
0 619
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1381
69.0%
0 619
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1381
69.0%
0 619
30.9%

Disability_Cognitive
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
1531 
1
469 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1531
76.5%
1 469
 
23.4%

Length

2025-05-27T14:35:55.274361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:55.348751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1531
76.5%
1 469
 
23.4%

Most occurring characters

ValueCountFrequency (%)
0 1531
76.5%
1 469
 
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1531
76.5%
1 469
 
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1531
76.5%
1 469
 
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1531
76.5%
1 469
 
23.4%

Disability_Physical
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
1933 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1933
96.7%
1 67
 
3.4%

Length

2025-05-27T14:35:55.439365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:55.524709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1933
96.7%
1 67
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 1933
96.7%
1 67
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1933
96.7%
1 67
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1933
96.7%
1 67
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1933
96.7%
1 67
 
3.4%

Disability_Sensory
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
1990 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1990
99.5%
1 10
 
0.5%

Length

2025-05-27T14:35:55.843065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:56.185390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1990
99.5%
1 10
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1990
99.5%
1 10
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1990
99.5%
1 10
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1990
99.5%
1 10
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1990
99.5%
1 10
 
0.5%

Disability_SocialEmotional
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
1927 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1927
96.4%
1 73
 
3.6%

Length

2025-05-27T14:35:56.473556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:56.603091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1927
96.4%
1 73
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 1927
96.4%
1 73
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1927
96.4%
1 73
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1927
96.4%
1 73
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1927
96.4%
1 73
 
3.6%

NCCD-Funded
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
1822 
1
 
178

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1822
91.1%
1 178
 
8.9%

Length

2025-05-27T14:35:58.081285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:58.145004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 1822
91.1%
1 178
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 1822
91.1%
1 178
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1822
91.1%
1 178
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1822
91.1%
1 178
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1822
91.1%
1 178
 
8.9%

NumSibling
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3565
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:58.207334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.99393244
Coefficient of variation (CV)0.42178334
Kurtosis1.7452873
Mean2.3565
Median Absolute Deviation (MAD)1
Skewness0.98135914
Sum4713
Variance0.9879017
MonotonicityNot monotonic
2025-05-27T14:35:58.288372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 911
45.6%
3 535
26.8%
1 339
 
17.0%
4 155
 
7.8%
5 36
 
1.8%
6 21
 
1.1%
7 3
 
0.1%
ValueCountFrequency (%)
1 339
 
17.0%
2 911
45.6%
3 535
26.8%
4 155
 
7.8%
5 36
 
1.8%
6 21
 
1.1%
7 3
 
0.1%
ValueCountFrequency (%)
7 3
 
0.1%
6 21
 
1.1%
5 36
 
1.8%
4 155
 
7.8%
3 535
26.8%
2 911
45.6%
1 339
 
17.0%

SiblingOrder
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7485
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:58.369621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86522948
Coefficient of variation (CV)0.494841
Kurtosis1.4846589
Mean1.7485
Median Absolute Deviation (MAD)1
Skewness1.1858534
Sum3497
Variance0.74862206
MonotonicityNot monotonic
2025-05-27T14:35:58.473854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 942
47.1%
2 718
35.9%
3 260
 
13.0%
4 64
 
3.2%
5 13
 
0.7%
6 3
 
0.1%
ValueCountFrequency (%)
1 942
47.1%
2 718
35.9%
3 260
 
13.0%
4 64
 
3.2%
5 13
 
0.7%
6 3
 
0.1%
ValueCountFrequency (%)
6 3
 
0.1%
5 13
 
0.7%
4 64
 
3.2%
3 260
 
13.0%
2 718
35.9%
1 942
47.1%

01.SES
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.9415
Minimum78
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:58.573538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile90
Q195
median101
Q3113
95-th percentile120
Maximum120
Range42
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.3859003
Coefficient of variation (CV)0.091177031
Kurtosis-0.84325767
Mean102.9415
Median Absolute Deviation (MAD)6
Skewness0.1772414
Sum205883
Variance88.095125
MonotonicityNot monotonic
2025-05-27T14:35:58.701053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
95 317
15.8%
114 181
 
9.0%
97 134
 
6.7%
101 117
 
5.9%
120 112
 
5.6%
94 105
 
5.2%
103 100
 
5.0%
116 82
 
4.1%
104 77
 
3.9%
113 73
 
3.6%
Other values (20) 702
35.1%
ValueCountFrequency (%)
78 21
 
1.1%
88 38
 
1.9%
89 25
 
1.2%
90 26
 
1.3%
91 40
 
2.0%
92 38
 
1.9%
93 19
 
0.9%
94 105
 
5.2%
95 317
15.8%
96 15
 
0.8%
ValueCountFrequency (%)
120 112
5.6%
117 41
 
2.1%
116 82
4.1%
115 31
 
1.6%
114 181
9.0%
113 73
3.6%
112 40
 
2.0%
110 23
 
1.1%
109 63
 
3.1%
108 24
 
1.2%

02.SES
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.1175
Minimum78
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2025-05-27T14:35:58.817546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile89
Q195
median101
Q3109
95-th percentile117
Maximum120
Range42
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.150167
Coefficient of variation (CV)0.089604299
Kurtosis-0.68948333
Mean102.1175
Median Absolute Deviation (MAD)6
Skewness0.24591591
Sum204235
Variance83.725557
MonotonicityNot monotonic
2025-05-27T14:35:58.931151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
95 281
 
14.1%
114 163
 
8.2%
97 137
 
6.9%
94 116
 
5.8%
101 111
 
5.5%
103 100
 
5.0%
120 96
 
4.8%
109 78
 
3.9%
105 77
 
3.9%
116 74
 
3.7%
Other values (21) 767
38.4%
ValueCountFrequency (%)
78 21
 
1.1%
87 10
 
0.5%
88 38
 
1.9%
89 48
 
2.4%
90 26
 
1.3%
91 28
 
1.4%
92 40
 
2.0%
93 48
 
2.4%
94 116
5.8%
95 281
14.1%
ValueCountFrequency (%)
120 96
4.8%
117 14
 
0.7%
116 74
3.7%
115 29
 
1.5%
114 163
8.2%
113 46
 
2.3%
112 40
 
2.0%
110 23
 
1.1%
109 78
3.9%
108 51
 
2.5%

NumAbvYear9
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
2
1405 
1
315 
0
279 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row2
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
2 1405
70.2%
1 315
 
15.8%
0 279
 
14.0%
3 1
 
0.1%

Length

2025-05-27T14:35:59.046933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:59.121458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 1405
70.2%
1 315
 
15.8%
0 279
 
14.0%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 1405
70.2%
1 315
 
15.8%
0 279
 
14.0%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1405
70.2%
1 315
 
15.8%
0 279
 
14.0%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1405
70.2%
1 315
 
15.8%
0 279
 
14.0%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1405
70.2%
1 315
 
15.8%
0 279
 
14.0%
3 1
 
< 0.1%

NumAbvDiploma
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
828 
2
601 
1
571 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 828
41.4%
2 601
30.0%
1 571
28.5%

Length

2025-05-27T14:35:59.215796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:59.291932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 828
41.4%
2 601
30.0%
1 571
28.5%

Most occurring characters

ValueCountFrequency (%)
0 828
41.4%
2 601
30.0%
1 571
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 828
41.4%
2 601
30.0%
1 571
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 828
41.4%
2 601
30.0%
1 571
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 828
41.4%
2 601
30.0%
1 571
28.5%

NumProf
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size113.4 KiB
0
947 
1
573 
2
480 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 947
47.3%
1 573
28.6%
2 480
24.0%

Length

2025-05-27T14:35:59.387670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:59.479905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 947
47.3%
1 573
28.6%
2 480
24.0%

Most occurring characters

ValueCountFrequency (%)
0 947
47.3%
1 573
28.6%
2 480
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 947
47.3%
1 573
28.6%
2 480
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 947
47.3%
1 573
28.6%
2 480
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 947
47.3%
1 573
28.6%
2 480
24.0%

Year_02
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size119.3 KiB
2020
609 
2018
495 
2017
379 
2021
295 
2016
222 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters8000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2018
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2020 609
30.4%
2018 495
24.8%
2017 379
18.9%
2021 295
14.8%
2016 222
 
11.1%

Length

2025-05-27T14:35:59.570491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T14:35:59.678589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2020 609
30.4%
2018 495
24.8%
2017 379
18.9%
2021 295
14.8%
2016 222
 
11.1%

Most occurring characters

ValueCountFrequency (%)
2 2904
36.3%
0 2609
32.6%
1 1391
17.4%
8 495
 
6.2%
7 379
 
4.7%
6 222
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2904
36.3%
0 2609
32.6%
1 1391
17.4%
8 495
 
6.2%
7 379
 
4.7%
6 222
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2904
36.3%
0 2609
32.6%
1 1391
17.4%
8 495
 
6.2%
7 379
 
4.7%
6 222
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2904
36.3%
0 2609
32.6%
1 1391
17.4%
8 495
 
6.2%
7 379
 
4.7%
6 222
 
2.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
False
1500 
True
500 
ValueCountFrequency (%)
False 1500
75.0%
True 500
 
25.0%
2025-05-27T14:35:59.769017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-05-27T14:35:46.985963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:54.095253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:58.948537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.655436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.995512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:07.123670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.829320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:12.060183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:18.557508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:21.838639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.274900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.478974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.909652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.114622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:33.747053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.953297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.345453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.573940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.778754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:47.152920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:54.440181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:59.155680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.790296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:04.116731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:07.285516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.952735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:12.178925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:18.736692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:22.005304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.389252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.589574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.027904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.228279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:33.922912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.082742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.461748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.712660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.885845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:47.307328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:54.756035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:59.581976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.909770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:04.236665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:07.458903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.070803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:12.298077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:18.918695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:22.184916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.511198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.702058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.142661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.340324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:34.105975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.205885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.595035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.829180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.996007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:47.458386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:55.001673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:59.691914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.020543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:04.374275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:07.629656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.184128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:12.407425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:19.080808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:22.361600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.621139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.854181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.250748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.463520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:34.292144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.327423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.709165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.942434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.103343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:47.626021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:55.194391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:59.810662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.134766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:04.563466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:07.804870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.298645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:12.525234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:19.247706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:22.533617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.753473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.151063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.365200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.572484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:34.461349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.461792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.832618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.055510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.215954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:47.773574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:55.519711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:59.920424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.250513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:04.729388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:07.972377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.414642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:12.633366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:19.420193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:22.696392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.862350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.313427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.477876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.704318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:34.625818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.581208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.943927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.162177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.326563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:47.927893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:55.715154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.043058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.363039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:04.900362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:08.151164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.525599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.052199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:19.582392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:22.814575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.980989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.432074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.596104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.841890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:36.297256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.709208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.062853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.275726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.431306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:48.084996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:55.913476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.158838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.497008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:05.064975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:08.331857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.645166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.168290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:19.736842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:22.924600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.094924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.534365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.704011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.978429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:36.511778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.828486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.179907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.382485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.540357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:48.267973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:56.222653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.289849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.616686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:05.230564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:08.511506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.761550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.285853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:19.897812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.037140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.212285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.644137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.823588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:32.093794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:36.674453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:38.951904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.300358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.508422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.647735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:48.438302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:56.558996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.417772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.732604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:05.394554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:08.625136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:10.884681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.404827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:20.083625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.146231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.323924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.757511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:29.948595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:32.205699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:36.792619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.080067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.410744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.622324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.775811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:48.599527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:56.824287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.534740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.854421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:05.563854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:08.756460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.014096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.511747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:20.258900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.255720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.436669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.878564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.065067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:32.328432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:36.905467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.210518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.529557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.748948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.883033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:48.750467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:57.228086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.655766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:02.973089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:05.731540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:08.881705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.128665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.627571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:20.452616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.365226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.544823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:27.990109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.174732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:32.443350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.023443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.331470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.649418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.852121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:45.993950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:48.898721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:57.444608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.778427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.094347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:05.910125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:08.994321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.244458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.739167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:20.616598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.476058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.653759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.099958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.287650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:32.555640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.133828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.470550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.771202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:43.977882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:46.107244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:49.084022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:57.656043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:00.903813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.235244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:06.083355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.105011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.366248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.849449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:20.787797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.583345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.784225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.212187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.410668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:32.679915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.250180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.615068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:41.883758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.090353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:46.219868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:49.235970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:57.843716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.025857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.359808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:06.249687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.219657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.477905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:17.961274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:20.949436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.714125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:25.901995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.317981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.526145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:32.856768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.359199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.737463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.002634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.199256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:46.324287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:49.401874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:58.064946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.160279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.521709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:06.431375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.352943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.594504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:18.085974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:21.127584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.829116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.025635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.440173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.643587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:33.072334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.497486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.868490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.126516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.323121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:46.443306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:49.550715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:58.304031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.284073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.641544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:06.607777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.473785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.711345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:18.207846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:21.294010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:23.945184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.144109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.546667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.758135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:33.247988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.603157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:39.995683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.243085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.433211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:46.553582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:49.700727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:58.517994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.414097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.755743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:06.792253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.590143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.825783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:18.325773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:21.465049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.059535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.254660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.664492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:30.865417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:33.407975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.722946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.117984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.353224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.547560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:46.672027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:49.857868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:34:58.703108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:01.527971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:03.876731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:06.959724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:09.710683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:11.949918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:18.434004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:21.664894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:24.164863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:26.365500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:28.772918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:31.005860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:33.584743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:37.839262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:40.233569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:42.464627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:44.649840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T14:35:46.835830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-27T14:35:59.886497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
01.SES02.SESAddition and Subtraction-01Addition and Subtraction-02At_Risk_NumeracyCounting-01Counting-02Disability_CognitiveDisability_Non-disableDisability_PhysicalDisability_SensoryDisability_SocialEmotionalGenderHRSIW-01-SOYKinder_AgeMultiplication and Division-01Multiplication and Division-02NCCD-FundedNumAbvDiplomaNumAbvYear9NumProfNumSiblingPlace Value-01Place Value-02SiblingOrderTextLevel-01-EOYTextLevel-01-MOYTextLevel-01-SOYTextLevel-02-EOYTextLevel-02-MOYTextLevel-02-SOYWritingVocab-01-SOYYear_02
01.SES1.0000.9650.1190.0770.2060.0590.1210.0530.0490.0380.0570.0350.0160.1500.1000.0590.0390.1610.1750.1140.216-0.0390.0920.145-0.0370.1550.1460.1150.1960.1970.1960.1670.288
02.SES0.9651.0000.1190.0700.1980.0450.1240.0300.0510.0460.0480.0250.0270.1350.1000.0470.0330.1130.1510.1010.208-0.0270.0940.147-0.0330.1570.1630.1290.1970.1910.1880.1620.306
Addition and Subtraction-010.1190.1191.0000.3810.3290.4370.3330.2340.2060.0240.0460.0000.0960.2970.1320.2240.2450.0810.0470.0160.058-0.0390.2640.317-0.0330.2860.3020.3430.2480.2560.2690.3630.044
Addition and Subtraction-020.0770.0700.3811.0000.3470.3180.4700.2310.2000.0250.0100.1070.1630.2530.0920.1240.3910.0700.0750.0500.082-0.0180.2030.435-0.0020.2800.2690.2790.2610.2900.2800.2670.036
At_Risk_Numeracy0.2060.1980.3290.3471.0000.2960.3440.2750.2510.0330.0620.0000.0000.3060.0330.2630.2580.1280.1260.1120.1150.0510.2840.3440.0270.3250.3300.3210.3490.3560.3520.3040.072
Counting-010.0590.0450.4370.3180.2961.0000.4170.2180.1920.0000.0000.0000.1170.3380.1140.1910.2290.0760.0450.0200.090-0.0290.3380.322-0.0120.3360.3550.4020.2460.2930.3070.3890.006
Counting-020.1210.1240.3330.4700.3440.4171.0000.2320.1920.0690.0000.0000.1880.2900.1030.1350.3400.0790.0720.0330.0790.0020.2040.505-0.0160.3200.3370.3660.2820.3430.3400.3450.017
Disability_Cognitive0.0530.0300.2340.2310.2750.2180.2321.0000.8250.0970.0210.1020.1060.3350.0640.2130.1610.4050.0340.0440.0510.0000.2120.2060.0000.3180.2810.3480.3180.3350.3170.3160.097
Disability_Non-disable0.0490.0510.2060.2000.2510.1920.1920.8251.0000.2740.0960.2870.1240.3000.0700.1690.1230.4610.0530.0600.0600.0000.1920.1690.0000.2930.2500.2900.2810.2940.2900.3200.100
Disability_Physical0.0380.0460.0240.0250.0330.0000.0690.0970.2741.0000.0000.0180.0000.0830.0310.0120.0430.0110.0300.0000.0000.0600.0710.0240.0550.0310.0860.0750.0000.0450.0520.0120.051
Disability_Sensory0.0570.0480.0460.0100.0620.0000.0000.0210.0960.0001.0000.0000.0260.0000.0490.0000.0000.0330.0000.0000.0140.0000.0000.0000.0430.0000.0330.0000.0000.0000.0000.0000.000
Disability_SocialEmotional0.0350.0250.0000.1070.0000.0000.0000.1020.2870.0180.0001.0000.0450.0340.0000.0000.0000.1670.0000.0000.0230.0170.0310.0000.0000.0420.0520.0000.0000.0000.0580.0630.035
Gender0.0160.0270.0960.1630.0000.1170.1880.1060.1240.0000.0260.0451.0000.0600.1150.0620.0540.0470.0490.0000.0260.0420.1520.1780.0070.0620.0320.0150.0000.0430.0380.1170.009
HRSIW-01-SOY0.1500.1350.2970.2530.3060.3380.2900.3350.3000.0830.0000.0340.0601.0000.0670.1290.2000.1900.1030.0860.099-0.0710.1990.273-0.0980.5140.5150.5860.4170.5080.5180.5750.204
Kinder_Age0.1000.1000.1320.0920.0330.1140.1030.0640.0700.0310.0490.0000.1150.0671.0000.0560.0810.0000.0000.0530.000-0.0050.0790.134-0.0310.0210.0710.0600.0220.0370.0340.1010.043
Multiplication and Division-010.0590.0470.2240.1240.2630.1910.1350.2130.1690.0120.0000.0000.0620.1290.0561.0000.1350.0830.0000.0250.0640.0000.1790.1430.0000.1020.1090.1120.0900.0970.0860.1270.040
Multiplication and Division-020.0390.0330.2450.3910.2580.2290.3400.1610.1230.0430.0000.0000.0540.2000.0810.1351.0000.0670.0490.0000.052-0.0150.1540.336-0.0080.2200.2130.2200.1830.2110.1980.2310.031
NCCD-Funded0.1610.1130.0810.0700.1280.0760.0790.4050.4610.0110.0330.1670.0470.1900.0000.0830.0671.0000.0320.0000.0380.0000.0750.0680.0000.1240.0760.1360.1410.1920.1770.1470.117
NumAbvDiploma0.1750.1510.0470.0750.1260.0450.0720.0340.0530.0300.0000.0000.0490.1030.0000.0000.0490.0321.0000.3970.4400.1110.0760.0640.0360.0970.1060.0880.1000.1080.0990.0890.113
NumAbvYear90.1140.1010.0160.0500.1120.0200.0330.0440.0600.0000.0000.0000.0000.0860.0530.0250.0000.0000.3971.0000.3480.1440.0500.0460.0410.0530.1000.0670.0560.0690.0760.0480.110
NumProf0.2160.2080.0580.0820.1150.0900.0790.0510.0600.0000.0140.0230.0260.0990.0000.0640.0520.0380.4400.3481.0000.1380.0990.1090.0300.1030.1140.0990.1050.1090.1050.0920.077
NumSibling-0.039-0.027-0.039-0.0180.051-0.0290.0020.0000.0000.0600.0000.0170.042-0.071-0.0050.000-0.0150.0000.1110.1440.1381.0000.000-0.0170.648-0.073-0.075-0.106-0.072-0.078-0.071-0.0180.011
Place Value-010.0920.0940.2640.2030.2840.3380.2040.2120.1920.0710.0000.0310.1520.1990.0790.1790.1540.0750.0760.0500.0990.0001.0000.2320.0000.1830.1810.2010.1670.1770.1780.1870.030
Place Value-020.1450.1470.3170.4350.3440.3220.5050.2060.1690.0240.0000.0000.1780.2730.1340.1430.3360.0680.0640.0460.109-0.0170.2321.000-0.0180.3370.3250.3540.2830.3280.3190.3100.048
SiblingOrder-0.037-0.033-0.033-0.0020.027-0.012-0.0160.0000.0000.0550.0430.0000.007-0.098-0.0310.000-0.0080.0000.0360.0410.0300.6480.000-0.0181.000-0.103-0.120-0.143-0.086-0.098-0.083-0.0570.018
TextLevel-01-EOY0.1550.1570.2860.2800.3250.3360.3200.3180.2930.0310.0000.0420.0620.5140.0210.1020.2200.1240.0970.0530.103-0.0730.1830.337-0.1031.0000.6990.6490.5860.7300.7790.5050.098
TextLevel-01-MOY0.1460.1630.3020.2690.3300.3550.3370.2810.2500.0860.0330.0520.0320.5150.0710.1090.2130.0760.1060.1000.114-0.0750.1810.325-0.1200.6991.0000.7200.5150.6330.6700.4870.082
TextLevel-01-SOY0.1150.1290.3430.2790.3210.4020.3660.3480.2900.0750.0000.0000.0150.5860.0600.1120.2200.1360.0880.0670.099-0.1060.2010.354-0.1430.6490.7201.0000.5040.6190.6260.5810.072
TextLevel-02-EOY0.1960.1970.2480.2610.3490.2460.2820.3180.2810.0000.0000.0000.0000.4170.0220.0900.1830.1410.1000.0560.105-0.0720.1670.283-0.0860.5860.5150.5041.0000.7320.6670.4110.075
TextLevel-02-MOY0.1970.1910.2560.2900.3560.2930.3430.3350.2940.0450.0000.0000.0430.5080.0370.0970.2110.1920.1080.0690.109-0.0780.1770.328-0.0980.7300.6330.6190.7321.0000.8260.4680.095
TextLevel-02-SOY0.1960.1880.2690.2800.3520.3070.3400.3170.2900.0520.0000.0580.0380.5180.0340.0860.1980.1770.0990.0760.105-0.0710.1780.319-0.0830.7790.6700.6260.6670.8261.0000.5080.110
WritingVocab-01-SOY0.1670.1620.3630.2670.3040.3890.3450.3160.3200.0120.0000.0630.1170.5750.1010.1270.2310.1470.0890.0480.092-0.0180.1870.310-0.0570.5050.4870.5810.4110.4680.5081.0000.049
Year_020.2880.3060.0440.0360.0720.0060.0170.0970.1000.0510.0000.0350.0090.2040.0430.0400.0310.1170.1130.1100.0770.0110.0300.0480.0180.0980.0820.0720.0750.0950.1100.0491.000

Missing values

2025-05-27T14:35:50.225731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-27T14:35:50.721129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.